The Transmission of Corporate Risk Culture:Evidence from Bank Acquisitions∗

Charles Boissel† Thomas Bourveau‡ Adrien Matray§

October 2015

Abstract

This paper examines the transmission of risk assessment practices related to future

loan defaults in the banking industry. Using bank acquisitions as shocks in a difference-

in-differences research design, we provide evidence that acquiring banking groups trans-

fer their discretionary risk assessment practices to newly acquired banking subsidiaries.

Specifically, we document an increase in the comovement between acquiring and tar-

get banks’ loan loss provisions following the acquisition that is not explained by banks’

underlying risk factors. We further perform additional tests to plausibly rule out reverse

causality and selection concerns. Overall, our findings shed light on how discretionary

risk assessment practices are transmitted, which is relevant to regulators trying to

assess factors affecting the systemic risk of the banking industry.

Keywords: Risk Practices, Bank Acquisition, Loan Loss Provision

JEL Classification: G21, G32, M41, M14

∗We are indebted to Robert Bushman, Miguel Duro-Rivas, Denis Gromb, Luigi Guiso, Robin Greenwood,Ulrich Hege, Mingyi Hung, Bin Ke, Anya Kleymenova, Evren Ors, Clemens Otto, Venky Nagar, DelphineSamuels, Jordan Schoenfeld and Chris Williams as well as workshop participants at the University of Illinoisat Chicago and the Hong Kong University of Science and Technology for helpful comments and discussions.All errors are our own.

†HEC Paris, Department of Finance - charles.boissel@hec.edu‡Hong Kong University of Science and Technology, Department of Accounting - actb@ust.hk§Princeton University, Department of Economics, Bendheim Center for Finance - amatray@princeton.edu

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1 Introduction

This paper examines how corporate culture affects managers’ discretionary choices of risk

assessment in the banking industry. The prevalent opinion among business press, regulators

and scholars is that inadequate culture is often to blame in large corporate scandals. Indeed,

“limit pushing values” and lack of monitoring processes have been cited as contributing

factors in the Enron case.1 Similar critiques applied to the recent crisis in the financial

industry. For example, Nobel Laureate Robert J. Shiller identified corporate culture, which

he refers to as “the spirit of the times”, has one of the driving forces behind the 2008-2009

financial crisis. In line with this statement, Fahlenbrach et al. (2012) find indirect evidence

that persistence in banks’ risk culture explains their performance during the recent crisis.

Despite such criticisms, the empirical literature in accounting, economics and finance has

not been able to fully quantify the role played by corporate culture in order to understand

firm policies. The lack of empirical results can be explained partly by the challenge of

carefully observing and quantifying corporate culture across organizations.2 The central

contribution of this paper is to develop an empirical model to identify plausibly exogenous

changes in corporate culture and test whether such changes explain variations in managers’

discretionary choices in the context of risk assessment.

Corporate culture has been defined in various ways. In economic theory, the importance

of corporate culture stems from contract incompleteness (Grossman and Hart, 1986). In this

context, corporate culture helps agents within firms to deal with situations with multiple

equilibria (Kreps, 1990). In this paper, we refer to corporate culture as all types of rules,

either formal or informal, explicit or tacit, used in an organization to shape agents’ behavior.

Specifically, we study risk culture, which refers to variations in risk-taking behavior across

organizations.

1See, for example, the article entitled “At Enron, Lavish Excess Often Came Before Success”, publishedin the Wall Street Journal on February 26, 2002.

2We acknowledge that there is a vast literature in the social sciences examining the role of corporateculture in organizations. We briefly review this literature in Section 2.

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We focus on the banking industry and examine one dimension that is particularly likely

to be subject to corporate risk culture: discretionary risk assessment, measured using loan

loss provisions (LLPs, hereafter). Loan loss provisions are non-cash expenses that repre-

sent a bank’s estimate of future loan losses. Such provisions are important because they

are associated with banks’ risk-taking profiles (e.g., Bushman and Williams, 2012, 2015).

Since there is no single way to estimate these provisions, managers’ subjectivity is likely to

be influenced by the rules, norms and preferences embedded in an organization. Indeed,

prior accounting studies establish that there is significant heterogeneity across banks in the

timeliness and accuracy of their LLPs (e.g., Liu and Ryan, 1995, 2006). Furthermore, prior

studies also show that banks use reporting discretion in LLPs for various reasons, including

circumventing capital adequacy requirements or smoothing earnings.3 As a result, LLPs,

which represent the largest accrual in a bank’s financial statement constitute a well-suited

corporate outcome to examine the transmission of risk culture.

To identify the impact of risk culture on banks’ discretionary choices about future loan de-

fault reporting choices, we rely on banking groups’ acquisitions of new banking subsidiaries.

Specifically, we develop and use a difference-in-differences design to test whether the comove-

ment in loan loss provision between a newly acquired subsidiary and the existing subsidiaries

of the acquiring banking group increases after the acquisition date. If the acquiring group

is able to impose its risk-assessment culture onto the target bank, we expect the LLPs of

the target bank to follow that of the acquiring bank more closely after the acquisition is

completed.

We retrieve bank balance-sheet data using the FED Call Reports database and identify

4,560 changes in ultimate ownership for our sample of U.S. banks over the 1976 - 2005 period.

We restrict our sample to the pre-2005 period to avoid capturing the effect of the financial

crisis on credit losses and the increased uncertainty about future loan defaults.

3Beatty and Liao (2014) provide a survey of the research on banks’ financial accounting. Specifically, theirSection 5 reviews the literature on banks’ financial reporting discretion and capital and earnings management.

3

We first establish that the comovement in LLPs between the target and the banking

group increases after the acquisition date and that the effect is permanent. In our regression

specifications, we control for a number of bank and state characteristics, and in particu-

lar the default rate of their loan portfolio that have been shown in the literature to affect

LLPs and that could plausibly affect the acquisition decision as well. We further corrobo-

rate our findings by adding various sets of fixed effects to our model. In the most stringent

specification, we remove any unobserved time-varying heterogeneity across states by adding

state-year fixed effects.4 Moreover, we find that the increase in comovement does not pre-

cede the acquisition itself. This suggests that our results are driven by the transmission of

risk culture within banking groups rather than by banking groups selecting targets whose

discretionary behavior is already similar ex ante to that of the group.

We next extend our analysis by following Angrist and Krueger (2001), who argue that

most exogenous shocks have an heterogeneous effect across affected subjects. We conduct

three sets of cross-sectional tests. First, we predict and find that our effect is more pro-

nounced when the acquiring and target banks are located in the same metropolitan area.

This finding is consistent with the literature in economic geography arguing that the trans-

mission of knowledge across firms and the ability to influence peers are enhanced by geo-

graphic proximity.5

Second, we partition our sample based on the ability of an acquirer to plausibly influence

the behavior of its target and find that greater bargaining power amplifies the effect. Using

relative size as a proxy for bargaining power, we find that our effect is smaller when the

size of the target is relatively large compared to that of the acquiring bank. This finding

4This set of fixed effects ensures that we remove any change in regulation and/or macroeconomic shocksat the state level that may affect both the number of bank acquisitions and the comovement in LLPs. We usestate-year fixed effects because, as shown by Gormley and Matsa (2014), using the average effects estimatorwhere the dependent variable is manually demeaned produces a biased estimate.

5The notion of geographic proximity is central in the agglomeration economic literature and innovationliterature studying “knowledge spillovers”. For surveys, see, for example, Audretsch and Feldman (2004)and Carlino and Kerr (2014). In the banking literature, a recent study by Gaspar (2015) provides plausiblycausal evidence that a reduction in distance between a bank’s headquarters and its subsidiaries improves themonitoring of the subsidiaries, which translates into higher performance.

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is consistent with previous studies in the M&A literature arguing that larger size serves

as an effective takeover defense (e.g., Masulis et al., 2007). This result is also in line with

experimental studies that document the existence of post-merger cultural clashes when the

size of the two merging entities is comparable (e.g., Weber and Camerer, 2003).

Third, we explore another dimension and study how our results vary with the organiza-

tional structure of the target bank. Indeed, prior research suggests that corporate culture

evolves with a firm’s organizational structure (e.g., Berson et al., 2008). Thus, we hypoth-

esize that a target bank’s risk culture is more likely to be closer to that of their future

acquiring banking group if it already belongs to an existing banking group. In lines with

this prediction, we find that our effect is amplified if the target is a stand-alone bank relative

to target banks that already belong to a banking group before the acquisition.

We next study in more detail what type of risk culture is transmitted to the target bank.

In particular, we classify banks as being “risk-taking” if their amount of LLPs is lower than

the one predicted by their economic characteristics, including And that of their loan portfolio.

Similarly, we classify banks as “risk averse” if their amount of LLPs is higher than the one

predicted by their characteristics.6 We find that following the acquisition, target banks adopt

a more aggressive risk assessment policy if their acquiring banking group is also following

aggressive reporting choices with respect to loan loss provisions. This particular finding is

important because it indicates that increase in concentration in the banking industry over

time lead “risk taking banks” in terms of reporting choices to impose similar risk culture to

more “risk averse” target banks after their acquisition.

Finally, we acknowledge that acquired banks may systematically differ from non-acquired

ones, and that some remaining unobservable characteristics may drive our results. That is,

it might be that some unknown factors driving the acquisition decision might lead the LLPs

of the target and acquiring banks to comove more even in the absence of the acquisition.

6To perform this classification, we compare the residuals of an estimation of the expected level of loanloss provisions based on banks’ economic characteristics following the models discussed in Beatty and Liao(2011).

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To account for this endogeneity concern, we use a matching technique. Specifically, we

match each acquired bank in our sample with another non-acquired bank to create a control

group of placebo target banks using Mahalanobis matching.7 Our analysis reveals that the

comovement in LLPs between our group of placebo target banks and acquiring banking

groups does not increase around the placebo acquisition date. This reduces the risk that our

results are driven by banks’ characteristics in the pre-acquisition period.

Our paper is related to four strands of literature. First, we contribute to the literature

on the role of corporate culture in organizations, while prior studies focus mostly on the role

of national culture, including in M&A settings (Ahern et al., 2012). Recent studies started

to quantify how corporate culture is associated with corporate policies and firm charac-

teristics (Cronqvist et al., 2009; Popadak, 2014; Guiso et al., 2015). Our paper innovates

along two main dimensions. First, our design allows us to make a plausible causal claim

and document that corporate culture is transferred from acquiring groups to target banks.8

Importantly, we find that the change in behavior does not precede the change in owner-

ship, which plausibly suggests that we capture changes in culture that occurred after the

acquisition. Second, we concentrate our analysis on the transmission of corporate culture in

financial institutions. Thus, our findings also relate to the growing literature investigating

banks’ characteristics during the recent financial crisis, including managers’ compensation

and risk incentives (Fahlenbrach and Stulz, 2011; Cheng et al., 2014). Other studies find

indirect evidence that corporate culture may explain banks’ performance sensitivity to eco-

nomic crises (Fahlenbrach et al., 2012) and that a common profit-oriented corporate culture

affects employees across multiple activities within financial institutions (Pacelli, 2015).

Second, our findings contribute to the accounting literature that examines the determi-

7We require our matched control bank to be located in the same state as the one that is actually acquiredand then match on observable characteristics in the year of the acquisition. We discuss our approach infurther detail in Section 6.

8In a related study, Fisman et al. (2015) provide causal evidence that cultural proximity between bankofficers and borrowers improves the efficiency of credit allocation. However, their study examines culturalproximity between contracting parties, while our paper focuses on corporate culture. Therefore, our paperis to our knowledge the first one to make a plausible causal claim regarding the effect of corporate cultureon firms’ decisions.

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nants of managers’ discretionary choices regarding LLPs. Prior studies document that bank

managers use the discretion allowed within accounting standards in their LLPs to manage

their reported regulatory requirements (Moyer, 1990; Beatty et al., 1995; Collins et al., 1995)

and that this behavior is concentrated in the pre-Basel period (e.g., Ahmed et al., 1999).

Other studies specifically examine the use of discretion in LLPs for earnings management

incentives to avoid a decrease in reported earnings (Beatty et al., 2002). Our findings high-

light that the transmission of corporate culture through acquisitions explains part of banks’

observed heterogeneity in their loan loss provisioning.

Third, our results speak to the accounting literature investigating the consequences of

banks’ loan loss provision choices. Recent studies highlight the negative consequences of

aggressive reporting choices for banks during economic downturns and its implications for

the stability of the financial industry in general (Bushman and Williams, 2012, 2015; Ng

and Roychowdhury, 2015). Our results highlight the role played by bank acquisitions in

turning more banks to opt for aggressive reporting choices, which may ultimately increase

the systemic risk of this industry.

Lastly, our paper relates to the literature studying the consequences of bank mergers.

Regarding prices, researchers have documented the unfavorable effects of increased market

concentration on deposit rates (Prager and Hannan, 1998), consumer loan rates (Kahn et al.,

2005), real-estate loan rates (Garmaise and Moskowitz, 2006) and commercial and industrial

loan rates (Sapienza, 2002; Erel, 2011). The effects of market concentration on efficiency in

the financial sector are more nuanced (e.g., Jayaratne and Strahan, 1998; Karceski et al.,

2005; Hombert and Matray, 2014).

The rest of the paper is organized as follows. We review the literature and develop our

hypotheses in Section 2. Section 3 describes the data sources and variables. In Section 4, we

present our empirical strategy. In Section 5, we report our main findings. Robustness tests

are discussed in Section 6. Section 7 concludes.

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2 Hypothesis Development

Corporate culture is often described by practitioners as an underestimated key factor in

organizational success.9 It has been defined in various ways, and the absence of a unified

definition stems from the challenge of precisely quantifying all its aspects. Formally, economic

theory defines corporate culture as a tool to help agents within firms dealing with situations

with multiple equilibria (Kreps, 1990; Hermalin, 2001).

In this paper, we focus on “risk culture”, which refers to the relevance of culture for

risk-taking choices within organizations / banks. This encompasses all types of formal and

informal guidance that influence employees’ behavior with respect to risk taking. As such,

this definition includes both formal control systems and informally shared values or beliefs

and is derived from the Competing Values Framework developed in the organization theory

literature (e.g., Quinn and Rohrbaugh, 1983; Quinn and Cameron, 1983). This particular

framework has been used recently by Thakor (2015) in his review of the literature on cor-

porate culture and its application to the financial sector. While risk culture is only one

component of a firm’s overall corporate culture, it is important to note that recent studies

argue that observed risk priorities that exist within an organization mirrors a corporate cul-

ture’s values (e.g., Lo, 2015). This indicates that if our analysis captures some form of risk

taking within banks, it is plausibly generalizable to banks’ overall corporate culture.

The common challenge to empirical studies is that corporate culture is difficult to quantify

in systematic ways for a large sample of firms. Prior studies have opted for various solutions

to examine the role of corporate culture. Early studies aimed at assessing variations in

culture across organizations often use cross-country comparisons (Hofstede et al., 1983).

Other researchers choose to use detailed within-organization case studies (e.g., Larcker and

Tayan, 2015). For larger samples of firms, prior studies usually rely on two types of construct

to quantify corporate culture. A first set of articles relies on observable CEO characteristics

9For example, see http://www.greatplacetowork.com/publications-and-events/blogs-and-news/2430-you-cant-legislate-a-smile.

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to proxy for the strength of a firm’s ethical values and relates this feature to various corporate

outcomes such as financial fraud. These characteristics include, for example, suspect options

backdating (Biggerstaff et al., 2015), managers’ taste for luxury products and/or their prior

legal infractions (Bushman et al., 2015; Davidson et al., 2015), and CEO military experience

(Benmelech and Frydman, 2015). A second set of studies quantifies corporate culture across

firms using surveys. For example, Guiso et al. (2015) use a novel dataset based on extensive

surveys of the employees of approximately 1,000 U.S. firms developed by the Great Place to

Work Institute. Next, they correlate the strength or features of corporate culture to firms’

characteristics.

The strength of the studies using surveys and/or CEO characteristics is that they quantify

one aspect or several aspects of a firm’s culture over large enough samples to use economet-

ric tools. The drawback of this approach is that it does not account for the endogenous

relationship between a firm’s culture and other corporate characteristics.10 In this paper,

we adopt a novel approach and develop an empirical model to account for the endogenous

relationship between corporate culture and firm policies. That is, instead of quantifying cor-

porate culture for an organization in a given year, we use bank acquisitions as a unexpected

change in corporate culture for newly acquired banks. We next examine whether the culture

of the acquiring bank in terms of risk assessment is transmitted to the acquired bank.11

Practitioners and scholars have stressed that the process of cultural transfer from ac-

quiring to target companies (i.e., acculturation) is part of firms’ post-acquisition integration

plans. However, the ability of an acquiring firm to transfer its culture remains unclear.

Indeed, prior studies in organization behavior document strong resistance to acculturation

processes (e.g., Nahavandi and Malekzadeh, 1988; Weber and Camerer, 2003). In this paper,

we first conjecture that banking groups engage in acquisitions and subsequently transfer their

10One exception is the study by Benmelech and Frydman (2015) that exploits exogenous variation in thepropensity to serve in the military as an instrument for CEO traits.

11We acknowledge that the choice of the target bank is unlikely to be random. In section 5, we provideevidence that target banks are not selected because they exhibit behavior similar to their future parentcompany’s before the acquisition. In section 6, we run additional tests to rule out the risk that our effectsprimarily reflect a selection problem.

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risk culture, including control systems and values related to risk, to newly acquired banking

subsidiaries as part of their post-acquisition integration plans to homogenize practices within

banking groups. We therefore formulate our main hypothesis:

Hypothesis 1: Acquiring banks transfer their corporate culture of risk assessment to

their acquired subsidiaries.

Next, we develop three cross-sectional predictions following Angrist and Krueger (2001),

who argue that most exogenous shocks have a heterogeneous effect across the treatment

sample. That is, if our main hypothesis is true, the transmission of risk culture through

acquisitions should vary in predictable ways across acquired banks.

There is a vast body of research in economics stressing the importance of geographic

proximity for the diffusion of information. This notion has been particularly important in

the urban economic literature, that identifies “knowledge spillovers” as one of the three

main reasons for the importance of agglomerations.12 Information diffuses locally in part

because physical proximity increases the ability of economic agents to exchange ideas and

learn about important incipient knowledge, in particular tacit knowledge (e.g., Jaffe et al.,

1993; Audretsch and Feldman, 1996; Matray, 2015). In the finance literature, geographic

proximity has also been identified as crucial in the diffusion of information in the case of

retail traders (Grinblatt and Keloharju, 2001; Coval and Moskowitz, 2001), analysts (Malloy,

2005), and institutional investors (Baik et al., 2010).

Prior studies also suggest that changes in agents’ preferences and/or beliefs occur through

repeated interactions (e.g., Guttman, 2003), which are facilitated by proximity. In the bank-

ing literature, a recent study by Gaspar (2015) relies on a plausibly causal setting and doc-

uments that a reduction in the distance between a bank’s headquarters and its subsidiaries

leads to improved monitoring. In the context of bank acquisitions, we then conjecture that

the transfer of culture from groups to newly acquired subsidiaries is facilitated by the geo-

12The other two reasons are the sharing of workers and the sharing of inputs. For surveys, the reader canrefer to Audretsch and Feldman (2004), Moretti (2004), Feldman and Kogler (2010) and Carlino and Kerr(2014).

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graphical proximity of the two organizations. This leads to our second hypothesis:

Hypothesis 2: The transmission of corporate culture in terms of risk assessment is

stronger when acquiring and target banks are located in the same geographical area.

The prevailing view among practitioners is that corporate culture largely explains failures

in M&A transactions. Specifically, insufficient compatibility between bidder and target firms’

cultures is said to offset the expected synergies of the deal.13 In line with this argument, Cai

and Sevilir (2012) find that board connectedness plays an important positive role in M&A

value creation. They suggest that such connections might help acquiring firms to assess

ex ante the compatibility of firms’ culture. M&A failures due to incompatible corporate

cultures may also arise because of employees’ post-merger actions. Indeed, prior studies

provide evidence of post-merger resistance to acculturation (Weber and Camerer, 2003; Yu

et al., 2005). As a result, studies also show that the ability of an acquirer to influence its

target depends on its bargaining power (Capron and Shen, 2007). In the context of M&A,

Masulis et al. (2007) argue that size serves as an effective takeover defense. That is, the larger

the target firm relative to the acquiring firm is, the more difficult it is for the acquiring firm

to impose its values and processes. As a result, we posit that the transmission of corporate

culture varies with the relative size of the target bank and formulate our third hypothesis:

Hypothesis 3: The transmission of corporate culture in terms of risk assessment is

stronger when the relative size of the target bank is smaller.

Finally, studies from the organization literature note that organizational characteristics

affects a firm’s corporate culture (e.g., Berson et al., 2008). As a result, we expect target

banks that belong to existing banking group to have developed a risk culture that is closer

to that of their future new parent banking group relative to stand-alone bank. This leads to

our final hypothesis:

Hypothesis 4: The transmission of corporate culture in terms of risk assessment is

stronger when the BHC acquires an independent target bank.

13See, for example, http://www.globoforce.com/gfblog/2012/6-big-mergers-that-were-killed-by-culture/

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3 Data

In this section, we describe our sample selection, explain the procedure we followed to

identify bank acquisitions, and present our data.

3.1 Data Sources

All banking institutions regulated by the Federal Deposit Insurance Corporation, the

Federal Reserve, or the Office of the Comptroller of the Currency file Reports of Condition

and Income, known as Call Reports. Call Reports include balance sheet and income data

on a quarterly basis and also report the identity of the entity that holds at least 50% of a

banking institution’s equity stake (RSSD9364 ), which we use to link banking subsidiaries to

their parent BHCs.

We restrict our sample period to the 1976 - 2005 period to avoid capturing the effect

of the recent financial crisis. Our research design is built upon the use of Bank Holding

Companies (BHCs, hereafter) subsidiaries’ balance sheets data. One challenge in our setting

is that ever since the enactment of the Riegle-Neal Act in 1995, BHCs have been allowed to

consolidate their balance sheets nationwide. This implies that after 1995, only a subset of

BHCs continued to report subsidiary level data.14

For each bank, we collect the amount of loan loss provisions (LLPs) (item riad4230 ) at

the end of each fiscal year. In our sample, we scale LLPs by banks’ total loans. We also

obtain data on total assets (item rcfd2170 ), total loans (item rcfd2122 ), real estate loans

(item rcfd1410 ), agricultural loans (item rcfd1590 ) and commercial and industrial loans

(item rcfd1600 ), as well as loans to individuals (item rcfd1975 ) and non-performing loans

(computed as the sum of items rcfd1403 and rcfd1407 ). We remove observations for which

the amount of loan loss provision is unavailable or negative. Finally, we retrieve information

on each bank’s state of location (item rssd9210 ) and its metropolitan statistical area (item

14To account for this empirical concern, we follow Landier et al. (2015) and perform a robustness test byrestricting our sample up to 1995. We discuss this specification in Section 6 and find that our results remainunchanged.

12

rssd9180 ).

We supplement our bank-level data with state-level data. Specifically, we obtain data

on state population and state population income from the Regional Tables of the Bureau of

Economic Analysis.

3.2 Acquisition-Level Variables

Our identification strategy relies on identifying banks that are acquired by another BHC

on a given date. To do so, we use the fact that all banks report their own BHC in the

call reports database (item rssd9348 ). To identify acquisitions, we then simply look for

changes in the reported BHC.15 Figure 1 plots the distribution of bank acquisitions over our

sample period. We only use the acquisitions between 1978 and 2003, in order to have at

least two years of data pre and post acquisition for all acquired banks in our sample. On

average, there are 226 acquisitions per year. The minimum number of acquisitions, 64, was

achieved in 2003. The maximum number of acquisitions, 411, occurred in 1986. Graphically,

we observe that banks’ acquisitions were more intense during the 1980s. This phenomenon

occurred as a response to the staggered adoption of state laws that allowed banks to expand

their activities both within state and across states (e.g., Jayaratne and Strahan, 1996).

For each acquisition, we identify all subsidiaries that were owned by the acquiring BHC

in the quarter preceding the acquisition. We then use this group of banks, to compare the

LLP comovement with the newly acquired bank before and after the acquisition date. To

do so, we compute the end of fiscal year mean of loan loss provisions of this group in the

period composed of the eight years before the acquisition year and the eight years following

the acquisition year. A subsidiary of this banking group remains in the group as long as the

ultimate BHC does not change.

Our final dataset is a panel of 4, 560 acquisitions of public and private banks where, for

each acquisition, we obtain the end of year LLP of the acquired bank, denoted i and the

15A bank that does not have a BHC is classified as an independent bank following Landier et al. (2015).

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average LLP of the other subsidiaries of its acquiring BHC, denoted j.16 We also follow

Landier et al. (2015) and identify the location of the acquiring BHC as the state in which it

has its largest share of assets the quarter preceding the acquisition.

3.3 Loan Loss Provision as a Proxy for Risk Culture

Throughout our analyses, we use loan loss provisions as a discretionary measure in the

context of risk assessment. The use of LLPs raises two concerns. First, to what extent are

LLPs discretionary? Second, do LLPs represent an item that is economically significant?

First, loan loss provisions represent an accrued expense that a bank sets aside to cover

potential losses on loans. Under U.S. GAAP, the accounting model for recognizing credit

losses is commonly referred to as an “incurred loss model”. Indeed, accounting guidance

requires only that banks estimate their provision using all observable data on probable

losses that have not occurred yet. Thus, critics often argue that such estimates are highly

subjective. To further gauge the subjectivity and variation inherent in banks’ loan loss

provisioning, the reader can refer to the discussions contained in the recent review paper by

Beatty and Liao (2011).

Second, loan loss provisions constitute the largest accrual in banks’ financial statements.

Consequently, the role of LLPs in the recent financial crisis has attracted attention from

regulators and standard setters. Indeed, a recent study by Ng and Roychowdhury (2015)

documents that loan loss provisions, which are added back into banks’ Tier 2 capital ratio,

are positively associated with bank failure risk. Other studies find that characteristics of

loan loss provisions are associated with the risk-taking profile of banks (e.g., Bushman and

Williams, 2012, 2015). As a result, there is currently a global debate about whether to shift

from an incurred loss model to an expected loss model to estimate loan loss provisions in a

more timely manner, which should enhance the stability of the financial system.

16To account for changes in the composition of the banking group, we focus only on the set of subsidiariesthat were owned by the BHC before the acquisition when we compute the average LLP.

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3.4 Summary Statistics

Our sample differs significantly from that used in the majority of previous accounting

studies since we study the entire universe of U.S. banks, while most studies examine the

behavior of publicly listed U.S. banks.17 Indeed, our objective is to maximize the size of

our sample to draw causal inferences and exploit variation in the characteristics of bank

acquisitions to strengthen our claim. As a result, our main sample contains 56,046 bank-

year observations for 4,560 U.S. banks that are acquired during our sample period. Target

banks are located in the same metropolitan statistical area (MSA, hereafter) in 11% of the

transactions.

Table 1 displays the summary statistics for our sample of 4, 560 acquisitions over the

1976 to 2005 period. This table reveals that, on average, acquiring banking groups are

composed of approximately 10 subsidiaries in the quarter preceding the acquisition and

make 15 acquisitions on average during our sample period. Target banks belongs to banking

groups that are, on average, composed of almost 4 subsidiaries. However, the distribution is

skewed since at the median, target banks are independent.

Table 2 displays the bank-level summary statistics for our main sample of 56,046 bank-

year observations over the 1976 to 2005 period. Target banks’ loan loss provisions represent,

on average, 0.59% of banks’ total loans. For our acquiring banking groups, the average LLPs

represent 0.54% of banks’ total loans. At the mean (median) of the distribution, banking

groups’ size (proxied by total assets) is $6.8 ($1.0) billion. For target banks, the average size

is $335 million, while the median is $57 million. Given the skewed distribution of bank size,

we take the logarithm of total assets in our regression analyses. These figures are generally

similar to those in Jiang et al. (2015), who examine the behavior of BHC as a response to

banking deregulation. However, in our sample the standard deviation and absolute values of

our growth variables, are larger (more volatile) presumably because our sample is composed

of target banks that are, on average, more than ten times smaller than their BHCs.

17See Beatty and Liao (2014) for a recent review of the accounting literature on banks.

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4 Identification Strategy

Our main research hypothesis is that risk evaluation practices are transmitted to banks

through acquisitions. We empirically test this conjecture by comparing the change in the

comovement of the LLPs between newly acquired banks and subsidiaries of the acquiring

banking group before and after the acquisition date, after controlling for various risk factors.

This approach treats acquisitions as shocks and builds on the work of Barberis et al. (2005)

and Boissel (2014).18 The central intuition is that this comovement should increase after

the acquisition date, since target banks start being influenced by the practices of acquiring

banking groups. Specifically, we estimate the following difference-in-differences model:

LLPi,t = LLP BHCj,t + Post Acquisitioni,t + LLP BHCj,t X Post Acquisitioni,t

+ Bank Controlsi,t + State Controlss,t

+ Controlsi,j × Post Acquisitioni,t + γt + ε (1)

In this model, i indexes acquired banks, j indexes acquiring BHC, s indexes state and t

indexes time. The dependent variable, LLPi,t, is the end of year loan loss provision of the

acquired bank i in year t. LLP BHCj,t is the end of year average loan loss provisions of all

subsidiaries already owned by the acquiring banking group j in the quarter preceding the ac-

quisition.19 Post Acquisition is an indicator variable that equals one after the acquisition of

bank i by the acquiring BHC j, and zero otherwise. In this model, θi,j represents acquisition

fixed effects and γt represents year fixed effects. Acquisition fixed effects are defined for each

acquisition event, i.e., each pair of a newly acquired bank and its acquiring BHC. Acquisition

18Barberis et al. (2005) use additions to the S&P 500 and find that increases in comovement betweenfirms’ beta and that of the S&P 500 index are not explained by changes in firms’ fundamentals but ratherby the role played by sentiment in financial markets.

19We use simple averages of all subsidiaries’ loan loss provisions for our main set of tests. However, inSection 6, we provide evidence that our results are robust to using weighted averages that take into accountthe relative size of subsidiaries within the banking group.

16

fixed effects capture time-invariant characteristics of the acquisition, such as bank-specific

shocks that could drive the decision to acquire a bank and future comovements in LLP.20

Year fixed effects absorb for aggregate shocks and common trends in M&A activity and LLP

decisions. Finally, in the most stringent specifications, we follow the recommendation of

Gormley and Matsa (2014) and augment our model with State × Year fixed effects. This

removes any time varying shocks and state characteristics that might affect banks’ acquisi-

tions and LLP decisions, including state business cycles and time-varying state institutional

differences (e.g., banking regulation, marginal tax rate).

The coefficient on LLP BHC captures the correlation between a target bank and its

future acquiring BHC before the acquisition took place. The coefficient on Post Acquisition

cannot be interpreted directly, since LLP BHC is a continuous variable.21 The variable

of interest is LLP BHC × Post Acquisition. Its coefficient corresponds to our difference-

in-differences estimate, that measures whether the LLP of a target bank comoves more or

less with that of an acquiring BHC following the acquisition. The identification relies on

comparing the correlation of LLP before and after the acquisition relative to a control group

of banks that have not been acquired yet. Our hypothesis predicts that this coefficient

should be positive and statistically significant to reflect a transfer of corporate culture in

risk practices between acquiring banking groups and target banks.22

It is important to note that in all our specifications, when controls are introduced, we also

add the controls interacted with the dummy Post Acquisition Controls × Post Acquisition.

This authorizes the effect of control variables to vary non parametrically after the acquisition.

20Note that acquisition fixed effects represent a more conservative approach relative to simply includingBHC fixed effects in our model. Indeed, BHC fixed effects would only remove time-invariant characteristicsfrom a given acquiring banking group for all the acquisitions performed by this group. As said, acquisitionfixed effects remove all time-invariant characteristics common to both target and acquiring banks. Thisnuance is important because we cannot rule out that a time-invariant unobservable characteristic commonto a target bank and its acquiring group drives the acquisition.

21Specifically, Post Acquisition captures the increase in comovement in LLPs after the acquisition if theloan loss provision of the BHC is equal to zero, which never occurs in our sample.

22In section 6, we implement another strategy to account for the possibility that acquired banks differsignificantly from non-acquired ones. Specifically, we use a matching algorithm to create a control group ofplacebo banks that are not acquired but share similar characteristics with acquired banks.

17

In particular, it allows us to take into account the possibility for underlying risks of loans to

vary after the acquisition, and makes sure that the change in LLP choice we observe is not

driven by a change in the loan portfolio.

Our source of variation in risk practices related to future expected loan defaults comes

from banking acquisitions. Thus, we cluster standard errors by acquisition.23 This clustering

method accounts for potential time-varying correlations in omitted variables that affect both

acquiring and target banks around the acquisition (Bertrand et al., 2004). We further add

two sets of control variables to our model. First, we include various bank level controls

that are known to be prime determinants of loan loss provisions and could plausibly affect

acquisition decisions as well. We follow the models described in Beatty and Liao (2014)

and incorporate banks’ leverage, size, loan growth, non-performing loan growth and loan

concentration to our model. It is important to note that those variables are meant to

capture banks’ underlying risk that affect the provisioning of future loan losses. As a result,

the coefficient on LLP BHC × Post Acquisition captures the discretionary increase in

comovement in LLP after the acquisition that is not explained by traditional underlying risk

factors. Second, we also include state-level controls to ensure that our results are not driven

by changes in local economic conditions rather than the acquisition itself and the induced

changes in risk evaluation practices. This list of control variables includes state population,

personal income, and personal income growth.

5 Results

5.1 Baseline Results

We start by providing a graphical illustration of the increase in LLPs’ comovement around

the bank acquisition date. To do so, we first compute the correlation between the LLPs of

the target bank and the LLPs of the acquiring banking group. The correlation is computed

23We find similar results if we cluster the standard errors either at the BHC or state level.

18

on a yearly basis, using a five-year centered moving window starting six years before the

acquisition and ending six years after.24 We then calculate the average correlation on a

given year relative to the acquisition year, and we plot it in Figure 3. Graphically, the

correlation is flat before the acquisition and rises sharply right after the acquisition. Note

that if it starts to increase two years before the acquisition date, this is simply because we

use a five year centered window to compute correlations. This clearly indicates that the

LLP comovement of a target and the subsidiaries of the acquiring BHC is strongly affected

after the acquisition, in line with our predictions. The correlation increases approximatively

threefold after the acquisition, from 0.1 to 0.27, an economically highly important effect.25

We next turn to our multivariate analyses and test our main hypothesis by formally

estimating the empirical model described in Equation (1). Table 3 displays the results. The

coefficient on LLP BHC × Post Acquisition is positive and statistically significant at the

1% level across all specifications, meaning that the comovement in LLPs between target and

acquiring banks increases significantly after the acquisition. In column (1), we report the

estimation of our model with acquisition fixed effects only. In column (2), our results hold

when we add year fixed effects, that absorb macroeconomic shocks. In column (3), we replace

year fixed effects by state × year fixed effects to account for time-varying unobservable events

at the state level, including changes in state regulation. Specifically, including state × year

fixed effects rules out the concern that our effects could be driven by heterogeneity in banking

deregulation across U.S. states. Finally, in column (4), we augment our model with bank

and state-level covariates and interact each control with our Post Acquisition dummy, to

capture in a flexible way all variations after the acquisition.26 Our results indicate that

target banks’ LLPs after the acquisition takes place, follow a pattern that is more similar

to that of their acquiring BHC, after we account for macro-economic shock and observable

24For example, in the year of the acquisition, we compute the correlation using the target’s and the BHCLLPs in years -2, -1, 0, +1 and +2, where year 0 refers to the acquisition year.

25Note that at this stage we cannot distinguish whether the increase in correlation is driven by increasesin the riskiness of banks’ assets and/or by a transmission of risk assessment practices.

26Throughout the paper we only report the main coefficients for the control variables for the ease ofpresentation but we systematically interact all control variables with the Post Acquisition dummy.

19

economic determinants of loan loss provisions.

Note that the coefficient on LLP BHC is positive and statistically significant across

our four specifications too. This indicates that there exists a pre-acquisition comovement

between target and acquiring banks. However, the magnitude of the effect is sharply re-

duced in column (3) when we introduce state × year fixed effects, while the magnitude

of the coefficient on LLP BHC × Post Acquisition remains unchanged. This suggests

that the pre-acquisition comovement is largely explained by local economic shocks while the

post-acquisition increase in comovement is likely driven by transmission of risk assessment

practices within banking groups.

Recall that in our main analysis, we examine the comovement in raw levels of loan loss

provisions between acquired and acquiring banks and do not use discretionary/abnormal

levels in LLPs. Thus, we need to control for risk factors that affect LLP decisions to ensure

that our results are not simply driven by a convergence in economic signals regarding future

loan defaults. The control variables reported in Table 3 carry the expected sign discussed

in Beatty and Liao (2014). For example, changes in non-performing loans are positively

related to contemporaneous levels of loan loss provisions.27 The coefficient on Log(Asset)

is not statistically significant, whereas it is positive and statistically significant in other

studies. This is due to the inclusion of acquisition fixed effects in our model, while the target

bank’s size is unlikely to vary significantly around the acquisition date. The coefficient on

Loan growth is negative and statistically significant as in the different models reported in

the Beatty and Liao (2014) survey paper. Furthermore, the coefficient on Personal income

growth is negative and statistically significant, consistent with the idea that increases in local

household income reduce the risk of future default on existing loans.

To gauge the magnitude of the effect, consider our most demanding specification from

Column (3) which includes state × year fixed effects. Our estimation shows that a one

27Indeed, for our sample of U.S. banks, the standards for LLPs are derived from an incurred loss model.That is, banks have to rely on observed factors that change the probability that loans will default. Thus,if loans are not independent from each other in a bank’s balance sheet, an increase in non-performing /defaulting loans likely predicts an increase in future defaults.

20

standard deviation increase in the acquiring BHC’s LLP leads to a rise of 0.09 (0.15 ∗ 0.64)

for that of the target bank, which corresponds to 15% of the target bank’s average LLP.

Our effect is therefore economically large and in line with Figure 3 in which we document

that the correlation between target banks’ and acquiring BHCs’ LLPs increases threefold,

on average, after the acquisition date.

One legitimate concern is that acquiring banks might select their target banks because

they have similar risk assessment practices. To rule out this endogeneity concern, we further

decompose our Post variable in year dummies around the acquisition date. We present

graphical evidence in Figure 4. Three important facts emerge. First, we observe that the

comovement in LLPs between acquiring banking groups and target bansk does not increase

before the acquisition date. This indicates that acquiring groups do not select banks with

increasingly similar risk practices before the transaction. Second, the increase in comovement

is not statistically different from zero until two years after the acquisition. This is consistent

with the idea in organization theory that it takes time to transmit corporate culture across

organizations. Third, we find that the effect is permanent. This rules out an additional

concern that acquiring banks might selectively acquire new banking subsidiaries to benefit

in the short-term from discretion in target bank’s risk assessments.28

We further corroborate our results with multivariate tests. Table 4 reports the results

of our estimation of Equation (1) with a decomposition of our effect.29 The coefficients

on the years t − 5 to t − 1 interacted with the loan loss provision of the BHC are not

statistically different from zero across the four specifications. This again indicates that the

increase in comovement in LLPs between target banks and acquiring banking groups was

not anticipated. The coefficient on years t and t + 1 are relatively small in magnitude and

not always statistically different from zero either, which suggests that the transmission of

corporate culture takes two years to be really effective. The coefficient of interest is then

28The benefits include earnings management and circumventing capital adequacy requirements.29In Table 4, we do not report the non-interacted coefficients on Post Acquisition and LLP BHC for ease

of presentation.

21

positive and statistically significant for years t+ 2 to t+ 5. Its magnitude is increasing over

time, suggesting that the corporate culture of the BHC slowly influences the risk assessment

practices of the newly acquired subsidiary. Finally, the results in Table 4 confirm that the

increase in LLP comovement is permanent, since the coefficient on years t + 6 and onward

is positive and statistically significant.

Another way to check for the possibility of endogeneity between LLP choice and acqui-

sition is to study if the distance in LLP between the target and the BHC before acquisition

can predict the year of the acquisition. If acquiring banking groups were able to identify po-

tential targets that have similar risk assessment policies and decide to acquire them because

of their cultural proximity, we should observe that the distance in LLPs predict the moment

of acquisition. To test if this is the case, we run the likelihood that the target is acquired

at year t on the distance in LLPs between the BHC and the target and include the same

controls and fixed effects as before. Reassuringly, the distance variable is never significant

(p-val=0.6), confirming that the proximity/distance in risk culture is not a dimension on

which BHC base their acquisition decisions (cf. Appendix C).30

5.2 Cross-Sectional Results

In the previous subsection, we present empirical evidence consistent with our first hypoth-

esis that corporate culture in the form of risk assessment practices is gradually transmitted

from acquiring groups to acquired banks. In this subsection, we follow Angrist and Krueger

(2001) who argue that the effect of exogenous sources of variation should vary predictably

across affected subjects. Thus, we further explore whether the transmission of risk assess-

ment practices is more pronounced for some specific sub-samples of banks, and we formally

test our second, third and fourth hypotheses.

30To compute the distance in LLp between acquiring banking groups and target banks we proceed intwo steps. We first estimate the residuals of a regression of observed level in loan loss provision on knowneconomic determinants (Beatty and Liao, 2014). Next, we select the residuals of the previous regression asthe fraction of LLP that is not explained by observable characteristics and compute the difference betweenthe residuals of the acquiring group and that of the target bank. We label this variable as the distance inLLP between acquiring groups and target banks.

22

First we test our second hypothesis that the transmission of corporate culture is more

pronounced when acquiring banking groups and target banks are located in the same ge-

ographical area. To do so, we create an indicator variable, Same MSA, that equals one if

acquiring BHCs and target banks are located in the same metropolitan statistical area, and

zero otherwise. This happens in 11.6% of the acquisitions in our sample. Table 5 reports

the results. The coefficient on LLP BHC × Post Acquisition is positive and statistically

significant at the 1% level across all specifications. This suggests that after the acquisition,

target banks’ LLPs follow a pattern that is closer to that of their acquiring BHC, when

target banks are located in a different MSA compared to their acquiring BHC. Furthermore,

the coefficient on LLP BHC × Post Acquisition × Same MSA is also positive and statis-

tically significant at the 1% level in the four specifications. This indicates that the increase

in the comovement in LLPs between acquiring banking groups and target banks is two times

stronger when both banks are located in the same MSA than when banks are not located

in the same MSA. It supports our second hypothesis, that geographical proximity enhances

the transmission of corporate culture in acquisitions.

Next, we test our third hypothesis, that the transmission of corporate culture is stronger

when the relative size of target banks is smaller. To do so, we first compute a continuous

variable, (Size Acquired)/(Size BHC), equal to the ratio of the target bank size over the

size of the acquiring BHC. Larger values indicate that the size of the target bank is higher

relative to that of its acquiring BHC. At the median, the size of the acquired bank represents

14.6% of that of its acquiring banking group. Table 6 displays our results. The coefficient

on LLP BHC × Post Acquisition is positive and statistically significant at the 1% level,

while the coefficient on LLP BHC × Post Acquisition × (Size Acquired)/(Size BHC) is

negative and statistically significant at the 1% level across all specifications. This suggests

that the increase in comovement between acquiring and target banks’ LLP is, on average,

smaller when the relative size of the target is high. Specifically, our analysis reveals that

moving from the 25th to the 75th percentile in terms of size ratio decreases the comovoment

23

in LLPs by 25%. The results in Table 6 are in line with our third hypothesis that the larger

the target bank is relative to the acquiring BHC, the more resistant it is to the transmission

of corporate culture in acquisitions.

Finally, we test our fourth and last hypothesis, that the transmission of culture in terms

of risk assessment practices will be more pronounced when stand-alone banks are acquired

by a banking group. To do so, we create a dummy variable Independent To Group, that

equal one if the acquirer is a group and the target a stand-alone bank, and zero otherwise.

Table 7 reports the results with the same four specifications we have used so far. Across all

specifications, the coefficient on LLP BHC × Post Acquisition × Independent To Group

is positive and statistically significant at the 1% level. In term of economic magnitude, the

coefficient is equal or slightly higher than the coefficient on LLP BHC × Post Acquisition,

which implies that the increase in comovement in LLPs is at least twice (sometimes three

times) larger when the target is a stand-alone bank and the acquirer is a group, relative to

other pairing possibilities. This finding supports our fourth hypothesis.

5.3 Aggressive Pairing

One remaining question pertains to the implication of this transmission of risk culture in

terms of general risk for the financial industry. In particular, does this increase in comove-

ment in LLPs increases or decreases the “aggressiveness” of the target bank? To answer this

question, we need to be able to identify “risk-taker / aggressive” banks and “risk-adverse /

conservative” banks. We rely on the accounting literature that studies the determinants of

LLPs and compute the residual of the LLPs after having controlled for our different known

economic determinants (in particular changes in non-performing loans and earnings before

loan loss provision).31 We identify a bank as “risk-taking” if the residual is negative, i.e if its

actual amount of LLPs is lower than the amount predicted by its economic characteristics.

Similarly, we consider a bank as being “risk-adverse” if the residual is positive, meaning that

31See for instance Beatty and Liao (2011) for a similar methodology.

24

the actual amount of LLPs of the bank is higher than the predicted amount.

After having classified banks has aggressive or conservative, we construct a dummy vari-

able Aggressive Pairing that equal one if the BHC was identified as “aggressive” and the

target is aggressive after the acquisition took place. Because the dependent variable mea-

sures directly the behavior of the target relative to the acquiring BHC, we only have to

include the variable Post Acquisition in the regression. The coefficient on this variable will

capture the extent to which following the acquisition, the target is more likely to become

aggressive if its acquiring banking group is also aggressive. Table 8 reports the results of

this regression. We find that on average, target banks are more likely to become aggressive

if the BHC is itself more aggressive after the acquisition. In term of economic magnitude,

the acquisition increases the probability that the target bank becomes more aggressive by

2 to 5 percentage points, which represents a relative increase of 5% to 10%.32 This addi-

tional result is important because prior researches in accounting have shown that aggressive

reporting related to future loan defaults was related to banks’ risk profile and their ability

to survive and to keep providing funding to the economy during economic downturns (e.g.,

Bushman and Williams, 2012; Ng and Roychowdhury, 2015). Our findings shed light on

the fact that banks’ acquisitions lead target banks to become more aggressive in terms of

reporting strategy, after accounting for the riskiness of their assets, which has implications

for the stability of the financial system.

6 Robustness Tests

In this section, we perform various additional tests to ensure the robustness of our main

findings and the validity of our research design to support a causal claim.

32Because the different bank-level controls have already been filtered out when we computed the residual,we do not need to reinclude the controls, which explains why column (4) has only state level controls.

25

Sample period Our dataset is a panel of banks from 1976 to 2005. As noted in Section

3.1, one challenge is that following the enactment of the Riegle-Neal Act in 1995, BHCs were

allowed to consolidate their balance sheets nationwide. This implies that after 1995, only a

subset of BHCs continued to report subsidiary-level data. To account for this concern, we

follow Landier et al. (2015) and perform a robustness test in which we restrict our sample

to the 1976 - 1995 period. In untabulated results, we find that the coefficient on LLP BHC

× Post Acquisition remains positive and statistically significant at the 1% level across all

four specifications in our baseline model. This indicates that our results are not affected by

variations in our sample period.

Matching Strategy One additional concern is that acquired banks may systematically

differ from non-acquired ones. In Section 5, the results in Figure 3 and Table 4 already

indicate that banking groups do not select target banks based on similar patterns in loan

loss provisions prior to the acquisition. That is, acquiring and target banks do not share

increasingly similar risk assessment cultures before the acquisition. However, one endogeneity

concern remains, since we cannot fully rule out the existence of a common factor between

target banks and yet-to-be-acquired target banks (our control group throughout the previous

section) that would lead to an increase in the comovement in LLPs between acquired and

acquiring groups that is not related to the acquisition itself.

To account for this endogeneity concern, we use a matching strategy to create an addi-

tional control group of non-acquired banks. Specifically, for each acquired bank we select its

nearest neighbor from the set of banks that are located in the same U.S. state and are not

acquired during our sample period. We match banks on all the controls use previously.33 We

require our matched banks to be economically comparable to their acquired counterparts,

which leads to a decrease by 50% in the number of unique acquisitions used to create this

33We follow Fresard and Valta (2015) and use a matching algorithm that reduces the Mahalanobis distanceacross treated and matched banks. Our results are qualitatively similar if we use a propensity score matchingtechnique.

26

additional control group.34 Appendix B presents the descriptive statistics for our sample of

acquired and matched banks. The univariate tests in differences between the two groups

suggest that the two groups are comparable since we fail to find any statistical difference.

We next examine whether the comovement in LLPs also increases between matched

banks and acquiring banking groups. We create an indicator variable, Treated, that equals

one for acquired banks, and zero otherwise. Table 9 reports the results. The coefficient on

LLP BHC × Post Acquisition is not statistically different from zero in the four specifica-

tions. This indicates that we fail to find an increase in the comovement in LLPs between

acquiring groups and the closest local neighbors to acquired banks. On the contrary, the

coefficient on LLP BHC × Post Acquisition × Treated is positive and statistically signif-

icant in all specifications. In short, we find that the comovement in LLPs increases only for

acquired banks and not for their matched counterparts. We interpret this result as evidence

that we capture the causal effect of the transmission of corporate culture from acquiring to

target banks rather than a spurious effect due to acquired banks’ characteristics.35

Regulation and Technology One concern is that our results could be driven by changes

in banking regulation. Indeed, a recent study by Jiang et al. (2015) provides evidence

that increased competition due to state-level banking deregulation leads to a decrease in

discretionary accruals for BHCs. We first rule out this concern by adding state × year fixed

effects in our model, that controls for time-varying changes in deregulation at the state level.

However, to further investigate this possibility, we perform an additional test and cut our

sample into two periods. Specifically, we split our sample based on whether acquisitions

where performed before or after 1990. The intuition for this test is that since the first set

of significant interstate deregulation events occurred in the late 1970s and in the 1980s, our

34Specifically, we drop matched and acquired banks for which the Mahalanobis distance between matchedand acquired banks is higher than 0.7. This criterion ensures that acquired and matched firms are statisticallycomparable in the year preceding the acquisition. However, in untabulated tests we find that our results arerobust to the inclusion of matched banks that are not fully statistically comparable before the acquisition.

35To be more specific, the causal interpretation of our results hinges on the absence of an unobservablefactor that is not related to any observable and risk factors of the target banks and that would still causethe increase in comovement in LLPs between acquiring and target banks absent the acquisition.

27

effect should be concentrated in the pre-1990 period if we are capturing primarily a change in

behavior driven by banking deregulation. However, in untabulated analyses we find that the

coefficients on LLP BHC × Post Acquisition are statistically significant at conventional

levels for both the pre-1990 period and the post-1990 period.

Discretionary loan loss provision In our analyses, we examine the change in comove-

ment in loan loss provision for acquiring and target banks around the acquisition date using

raw levels of LLPs. An alternative methodological choice is to follow a two-stage process.

Indeed, other accounting studies usually first predict the level of LLP using observable char-

acteristics and then use the residuals of this regression as the discretionary / unexplained

level of LLP in their tests (see Section 5 in Beatty and Liao (2014) for a review of such

models). In Table 10, we repeat our main analysis except that we replace target and ac-

quiring banks’ LLPs in our model with the unexplained part of LLPs used to plot Figure

2.36 Our results remain unaffected.37 This means that in our previous tests, we document

an increase in the comovement in LLPs that is not explained by observable bank and state

characteristics. In other words, we capture the effect of corporate culture on bank managers’

discretion in assessing provisions for future loan losses.

In our main model, we include the change in non-performing loans in t and t−1 to account

for risk factors / signals / economic determinants about loans’ future default probability.

In their review paper, Beatty and Liao (2014) discuss various specifications in the models

used in the accounting literature to predict the expected level of loan loss provisions. To

ensure the robustness of our findings, we repeat our main analyses and include the following

additional regressors: change in non-performing loans in t − 2, change in non-performing

loans in t + 1, non-performing loans in level in t and t-1. Our results remain unchanged

36Specifically, we follow Beatty and Liao (2011) and regress the level of LLPs on changes in non-performingloans in year t and year t − 1.We also add earnings before loan loss provision in our model. However, wedo not include the Tier I risk-adjusted capital ratio in our model, since this information is not available forprivate banks throughout our sample period.

37This is expected, as the two approaches are in fact similar, since in our main tests we explicitly controlfor the determinants used to predict normal / expected level of LLPs.

28

(untabulated).

Measure of loan loss provision In our analyses, we use as a covariate the average LLP

of acquiring banking groups computed as the simple average of LLP for all subsidiaries of

this banking group already owned by the BHC in the year before the acquisition. As a

robustness test, we compute this variable as the weighted average of LLP of all subsidiaries

already owned by the BHC prior to the acquisition, using subsidiary size (total assets) as

a weighting criterion. Table 11 displays the results. The coefficient on Post Acquisition ×

LLP BHC is positive and statistically significant at the 1% level across all specifications,

indicating that our results are not affected by our methodological choice in computing the

BHC average loan loss provision.38

Public versus Private One final concern would be that our results are driven by changes

in demand for reporting characteristics driven by acquisitions of private banks by public

banks with different use of accounting numbers, which may not necessarily capture changes

in risk culture relative to future loan default. In our sample, 51% of the acquisitions are

completed by public acquiring banks. In untabulated analyses, we repeat our main tests by

our main model for public and private acquiring banks separately. Our results hold and are

similar for both groups, suggesting that our effect is not solely driven by acquisitions from

public BHC.

7 Conclusion

In this paper, we attempt to shed light on how corporate risk culture is transmitted across

organizations. To do so, we use bank acquisitions and provide plausibly causal evidence of

a transmission of corporate culture in terms of risk assessment from acquiring groups to

acquired banking subsidiaries. Specifically, we find an increase in comovement in target and

38Note that we do not take the raw level of the BHC’s LLP directly, since we want to compare the changeof comovement in LLP between the already owned subsidiaries and the newly acquired one.

29

acquiring banks’ loan loss provisions after the acquisition. We perform multiple robustness

tests and ensure that our results are unlikely to be explained by reverse causality and selection

concerns. Our results are relevant to regulators who attempt to circumvent risky behavior

in the financial industry that could jeopardize the stability of financial markets.

30

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36

Figure 1: Distribution of Bank Acquisitions over Time

The figure shows the number of acquisitions for each year in our sample. The acquisitions are determined by

changes in ultimate ownership using the Bank Holding Company item of the FED Call Reports database.

In our main analysis, the sample period is 1976 to 2005. We thus restrict our sample of acquisitions to the

1978 to 2003 period to ensure that we have at least two years of data pre and post acquisition for all target

banks in our sample.

010

020

030

040

050

0N

b. O

f Acq

uisi

tions

Per

Yea

r

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004Year of Acquisition

37

Figure 2: LLP Correlation around Bank Acquisition

The figure shows the evolution of the average correlation in LLPs between the target and other subsidiaries of

the acquiring banking group around the year of acquisition. The correlation is computed between the target

LLP and the average LLP of all the subsidiaries of the acquiring banking group. We do it using a five-year

centered window for each acquisition. We then take the average correlation across all our observations for

each year around the acquisition date starting six years before the acquisition and ending six years after the

acquisition.

.1.1

5.2

.25

.3A

vera

ge C

orre

latio

n in

LLP

6 Y. Bef. Acqui.

5 Y. Bef. Acqui.

4 Y. Bef. Acqui.

3 Y. Bef. Acqui.

2 Y. Bef. Acqui.

1 Y. Bef. Acqui.

1st Y. After Acqui.

2 Y. After Acqui

3 Y. After Acqui

4 Y. After Acqui

5 Y. After Acqui

6 Y. After Acqui

38

Figure 3: Loan Loss Provisions Comovement around Acquisition

This figure shows the evolution of comovement in LLP between acquiring banks and target banks around theacquisition date. The specification is the same as in Equation (1) except that the Post Acquisition variable isreplaced by a collection of variables, Acquisition(k), where Acquisition(k) is a dummy equal to one exactlyk years after (or before if k is negative) the BHC acquires the target bank. The solid line plots the pointestimates for k = −6, . . . , 6, using the acquisition years k < 6 as the reference years. The dashed lines plotthe 95% confidence interval.

-.1

0.1

.2.3

Com

ovem

ent i

n LL

P

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

Years to Acquisition

39

Table

1:

Sum

mary

Sta

tist

ics

-A

cquis

itio

ns

Th

ista

ble

pre

sents

the

des

crip

tive

stat

isti

csfo

rou

rsa

mp

leof

ban

kin

gacq

uis

itio

ns

over

the

1976

to2005

per

iod

.

Mea

nP

25P

50P

75S.D

.T

arge

tB

ank

Tot

alL

oans

(Million

s)19

1.73

713

.262

28.6

3764

.527

1,64

0.15

0A

cquir

ing

Ban

kT

otal

Loa

ns

(Million

s)4,

090.

341

93.4

0756

6.59

62,

703.

181

1364

5.46

1N

um

ber

Subsi

dia

ries

per

Acq

uir

ing

Ban

k10

.349

1.00

04.

000

13.0

0013

.475

Num

ber

Subsi

dia

ries

per

Tar

get

Ban

k3.

767

1.00

01.

000

2.00

07.

813

Num

ber

ofM

erge

rsP

erA

cquir

ing

Ban

k15

.205

2.00

08.

000

19.0

0020

.014

Dum

my

Indep

enden

tT

arge

tA

cquir

edby

Gro

up

0.47

40.

000

0.00

01.

000

0.49

9B

HC

Tar

get

Tot

alA

sset

s/A

cquir

ing

Ban

kT

otal

Ass

ets

(%)

0.33

20.

047

0.14

60.

405

0.46

6D

um

my

Sam

eM

SA

Tar

get/

Acq

uir

ing

Ban

k0.

121

0.00

00.

000

0.00

00.

326

Agg

ress

ive

Pai

ring

0.40

10.

000

1.00

01.

000

0.49

1O

bse

rvat

ions

4,56

0

40

Table

2:

Sum

mary

Sta

tist

ics

-B

ank

Panel

Th

ista

ble

pre

sents

the

des

crip

tive

stat

isti

csfo

rou

rp

an

elof

ban

k-y

ear

ob

serv

ati

on

sov

erth

e1976

to2005

per

iod

.

Mea

nP

25P

50P

75S.D

.T

arge

tL

LP

/Tot

alL

oans

(%)

0.59

00.

164

0.38

30.

795

0.59

5T

arge

tT

otal

Ass

ets

(Million

s)0.

335

0.02

80.

057

0.12

32.

709

Tar

get

Tot

alL

oans

(Million

s)23

6.03

514

.154

30.5

3772

.003

2,72

1.53

3T

arge

tL

oan

Gro

wth

(%)

8.89

30.

000

6.70

815

.621

17.8

96T

arge

tN

onP

erfo

rmin

gL

oans

Gro

wth

(%)

-5.3

04-5

5.68

6-0

.970

42.2

3610

8.64

9T

arge

tL

ever

age

(%)

8.45

86.

838

7.91

69.

422

2.66

6T

arge

tL

oan

Con

centr

atio

n0.

404

0.34

00.

389

0.46

30.

134

Acq

uir

ing

Ban

k’

Subsi

dia

ries

Ave

rage

LL

P/T

otal

Loa

ns

(%)

0.53

80.

241

0.44

10.

722

0.45

5A

cquir

ing

Ban

kT

otal

Loa

ns

(Million

s)4,

138.

164

91.8

8962

4.01

02,

970.

048

1269

4.36

6A

cquir

ing

Ban

kT

otal

Ass

ets

(Million

s)6,

760.

806

163.

205

1,06

1.07

65,

186.

879

1999

2.27

4Sta

teP

opula

tion

(Million

s,L

og)

15.5

1715

.007

15.4

5916

.249

0.82

2Sta

teP

erso

nal

Inco

me

(Million

s,L

og)

18.1

3217

.539

18.1

4218

.818

0.92

3Sta

teP

erso

nal

Inco

me

Gro

wth

6.88

54.

897

6.32

18.

532

2.96

0D

ista

nce

inL

LP

s0.

003

-0.0

010.

001

0.00

50.

013

Obse

rvat

ions

56,0

46

41

Table 3: Baseline Results

This table compares loan loss provisions of target banks to those of their acquiring bank holdingcompanies over the 1976 - 2005 period. The dependent variable, LLP Target, corresponds tothe loan loss provision of target banks. Post Acquisition is an indicator variable that equals oneafter the acquisition of a target bank by another BHC, and zero otherwise. LLP BHC is equalto the average loan loss provision of all banking subsidiaries composing the acquiring BHC inthe year before the acquisition. All other variables are defined in Appendix A. Standard errorsare clustered at the acquisition level. ***, **, and * indicate statistical significance at the 1%,5% and 10% level, respectively.

Dependent Variable: LLP Target(1) (2) (3) (4)

LLP BHC 0.3468*** 0.2024*** 0.0664*** 0.1799***(0.0133) (0.0135) (0.0137) (0.0140)

Post Acquisition -0.0019*** -0.0017*** -0.0015*** -0.0037**(0.0001) (0.0001) (0.0001) (0.0017)

LLP BHC × Post Acquisition 0.2018*** 0.1892*** 0.1777*** 0.1327***(0.0175) (0.0172) (0.0182) (0.0183)

Leverage -0.0359***(0.0031)

Log(Asset) 0.0003**(0.0001)

Loan Growth -0.0060***(0.0003)

Loan Concentration -0.0003(0.0006)

Non Performing Loans Growth 0.0005***(0.0000)

Non Performing Loans Growth (t-1) 0.0007***(0.0001)

Population -0.0062***(0.0023)

Personal Income 0.0028(0.0017)

Personal Income Growth -0.0164***(0.0023)

Observations 56,046 56,046 56,046 56,046

R-Square 0.31 0.36 0.42 0.41

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year FE - - Yes -

42

Table 4: Baseline Results - Decomposition

This table compares loan loss provisions of target banks to those of their acquiring bank holdingcompanies over the 1976 - 2005 period. The dependent variable, LLP Target, correspond to theloan loss provision of target banks. We break the Post Acquisition dummy with paired yearlydummies around the acquisition date. LLP BHC is equal to the average loan loss provisionof all banking subsidiaries composing the acquiring BHC in the year before the acquisition.The non-interacted LLP BHC and Post Acquisition variables are not reported for ease ofpresentation. All other variables are defined in Appendix A. Standard errors are clustered atthe acquisition level. ***, **, and * indicate statistical significance at the 1%, 5% and 10%level, respectively.

Dependent Variable: LLP Target(1) (2) (3) (4)

LLP BHC × Post Acquisition (t-5,t-3) -0.0753* -0.0425 -0.0242 -0.0125(0.0378) (0.0314) (0.0225) (0.0249)

LLP BHC × Post Acquisition (t-2,t-1) -0.0119 -0.0091 0.0034 -0.0112(0.0377) (0.0318) (0.0269) (0.0328)

LLP BHC × Post Acquisition (t,t+1) 0.0686* 0.0536 0.0678** 0.0672**(0.0400) (0.0388) (0.0307) (0.0307)

LLP BHC × Post Acquisition (t+2,t+3) 0.1863*** 0.1877*** 0.1903*** 0.1803***(0.0590) (0.0476) (0.0352) (0.0348)

LLP BHC × Post Acquisition (t+4,t+5) 0.2442*** 0.2555*** 0.2436*** 0.2335***(0.0540) (0.0349) (0.0271) (0.0296)

LLP BHC × Post Acquisition (≥t+6) 0.2158*** 0.2254*** 0.2261*** 0.2410***(0.0461) (0.0320) (0.0317) (0.0340)

Observations 56,046 56,046 56,046 56,046

R-Square 0.32 0.36 0.42 0.48

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year FE - - Yes -

Bank-Controls - - - Yes

State-Controls - - - Yes

43

Table 5: Cross-Sectional Results - Geographic Proximity

This table compares loan loss provisions of target banks to those of their acquiring bank holdingcompanies over the 1976 - 2005 period. The dependent variable, LLP Target, corresponds tothe loan loss provision of target banks. Post Acquisition is an indicator variable that equalsone after the acquisition of a target bank by another BHC, and zero otherwise. LLP BHCis equal to the average loan loss provision of all banking subsidiaries composing the acquiringBHC in the year before the acquisition. Same MSA is an indicator variable equal to one if theacquiring banking group and its target bank are located in the same metropolitan statisticalarea. The single term is absorbed by the Acquistion FE. All other variables are defined inAppendix A. Standard errors are clustered at the acquisition level. ***, **, and * indicatestatistical significance at the 1%, 5% and 10% level, respectively.

Dependent Variable: LLP Target(1) (2) (3) (4)

LLP BHC 0.3702*** 0.2201*** 0.0789*** 0.2020***(0.0142) (0.0142) (0.0143) (0.0152)

Post Acquisition -0.0019*** -0.0017*** -0.0015*** -0.0036**(0.0001) (0.0001) (0.0001) (0.0018)

LLP BHC × Same MSA -0.1963*** -0.1400*** -0.0929** -0.1275***(0.0412) (0.0400) (0.0400) (0.0396)

Post Acquisition × Same MSA 0.0000 -0.0002 -0.0002 -0.0026(0.0003) (0.0003) (0.0003) (0.0053)

LLP BHC × Post Acquisition 0.1893*** 0.1787*** 0.1678*** 0.1312***(0.0190) (0.0186) (0.0197) (0.0203)

LLP BHC × Post Acquisition × Same MSA 0.1081** 0.0876** 0.0786* 0.0757*(0.0469) (0.0438) (0.0434) (0.0455)

Observations 56,046 56,046 56,046 56,046

R-Square 0.31 0.36 0.42 0.40

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year FE - - Yes -

Bank-Controls - - - Yes

State-Controls - - - Yes

44

Table 6: Cross-Sectional Results - Size Ratio

This table compares loan loss provisions of target banks to those of their acquiring bank holdingcompanies over the 1976 - 2005 period. The dependent variable, LLP Target, corresponds tothe loan loss provision of target banks. Post Acquisition is an indicator variable that equals oneafter the acquisition of a target bank by another BHC, and zero otherwise. LLP BHC is equalto the average loan loss provision of all banking subsidiaries composing the acquiring BHC inthe year before the acquisition. (Size Acquired) / (Size BHC) is a continuous variable equal tothe ratio of the target bank’s size over the acquiring banking group’s size. The single term isabsorbed by the Acquistion FE. All other variables are defined in Appendix A. Standard errorsare clustered at the acquisition level. ***, **, and * indicate statistical significance at the 1%,5% and 10% level, respectively.

Dependent Variable: LLP Target(1) (2) (3) (4)

LLP BHC 0.3494*** 0.1917*** 0.0499*** 0.1689***(0.0136) (0.0134) (0.0137) (0.0143)

Post Acquisition -0.0019*** -0.0018*** -0.0016*** -0.0040**(0.0001) (0.0001) (0.0001) (0.0017)

LLP BHC × (Size Acquired)/(Size BHC) -0.0014 0.0559*** 0.0739*** 0.0516**(0.0231) (0.0227) (0.0212) (0.0218)

Post Acquisition × (Size Acquired)/(Size BHC) -0.0004** 0.0001 0.0001 0.0001(0.0002) (0.0002) (0.0002) (0.0002)

LLP BHC × Post Acquisition 0.2001*** 0.2021*** 0.1953*** 0.1468***(0.0173) (0.0170) (0.0182) (0.0184)

LLP BHC × Post Acquisition × (Size Acquired)/(Size BHC) -0.1449*** -0.1591*** -0.1353*** -0.1341***(0.0328) (0.0320) (0.0316) (0.0303)

Observations 56,046 56,046 56,046 56,046

R-Square 0.32 0.36 0.42 0.41

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year - - Yes -

Bank-Controls FE - - - Yes

State-Controls - - - Yes

45

Table 7: Cross-Sectional Results - Independent Banks Acquired by Groups

This table compares loan loss provisions of target banks to those of their acquiring bank holdingcompanies over the 1976 - 2005 period. The dependent variable, LLP Target, corresponds tothe loan loss provision of target banks. Post Acquisition is an indicator variable that equalsone after the acquisition of a target bank by another BHC, and zero otherwise. LLP BHCis equal to the average loan loss provision of all banking subsidiaries composing the acquiringBHC in the year before the acquisition. Independent To Group variable is an indicator variableequal to one if the target bank isn’t the subsidiary of a banking group, e.g. is independent, andthe acquiring bank holding company owns more than one subsidiary on the quarter before theacquisition. The single term is absorbed by the Acquistion FE. All other variables are definedin Appendix A. Standard errors are clustered at the acquisition level. ***, **, and * indicatestatistical significance at the 1%, 5% and 10% level, respectively.

Dependent Variable: LLP Target(1) (2) (3) (4)

LLP BHC 0.3420*** 0.2096*** 0.0796*** 0.1854***(0.0172) (0.0173) (0.0172) (0.0174)

Post Acquisition -0.0019*** -0.0016*** -0.0014*** -0.0016(0.0002) (0.0002) (0.0002) (0.0016)

LLP BHC × Independent To Group 0.0118 -0.0164 -0.0339 -0.0197(0.0270) (0.0257) (0.0246) (0.0268)

Post Acquisition × Independent To Group -0.0003 -0.0004** -0.0004** -0.0004*(0.0002) (0.0002) (0.0002) (0.0002)

LLP BHC × Post Acquisition 0.1298*** 0.1247*** 0.1243*** 0.0878***(0.0227) (0.0219) (0.0215) (0.0223)

LLP BHC × Post Acquisition × Independent To Group 0.1736*** 0.1579*** 0.1364*** 0.1372***(0.0349) (0.0337) (0.0338) (0.0345)

Observations 56,046 56,046 56,046 56,046

R-Square 0.31 0.36 0.42 0.41

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year FE - - Yes -

Bank-Controls - - - Yes

State-Controls - - - Yes

46

Table 8: Aggressive Pairing

This table compares the likelihood that the target becomes more “aggressive” (residual in LLPsnegative) if the acquiror is more aggressive over the 1976 - 2005 period. The dependent variable,Aggressive Pairing is a dummy variable that equals one if the target and the acquiring BHChave negative residual LLPs. Post Acquisition is an indicator variable that equals one afterthe acquisition of a target bank by another BHC, and zero otherwise. All other variables aredefined in Appendix A. Standard errors are clustered at the acquisition level. ***, **, and *indicate statistical significance at the 1%, 5% and 10% level, respectively.

Dependent Variable: Aggressive Pairing(1) (2) (3) (4)

Post Acquisition 0.0561*** 0.0370*** 0.0189** 0.0216***(0.0064) (0.0088) (0.0086) (0.0087)

Population 0.0442(0.1884)

Personal Income 0.1653(0.1568)

Personal Income Growth 2.4057***(0.1807)

Observations 56,046 56,046 56,046 56,046

R-Square 0.152 0.163 0.277 0.197

Acquirer FE - Yes - Yes

Year FE - - Yes -

State-Year FE - - - Yes

47

Table 9: Robustness Test - Matching Procedure

This table compares loan loss provisions of target banks to those of their acquiring bank holdingcompanies over the 1976 - 2005 period. The dependent variable, LLP Target, correspond tothe loan loss provision of target banks. Post Acquisition is an indicator variable that equalsone after the acquisition of a target bank by another BHC, and zero otherwise. LLP BHCis equal to the average loan loss provision of all banking subsidiaries composing the acquiringBHC in the year before the acquisition. Treated is an indicator variable equal to one if a bankis acquired and zero for its matched counterpart. All other variables are defined in AppendixA. Standard errors are clustered at the acquisition level. ***, **, and * indicate statisticalsignificance at the 1%, 5% and 10% level, respectively.

Dependent Variable: LLP Target(1) (2) (3) (4)

LLP BHC 0.3131*** 0.1689*** 0.0275* 0.1444***(0.0171) (0.0157) (0.0147) (0.0137)

Post Acquisition -0.0015*** -0.0004** -0.0004*** -0.0043***(0.0001) (0.0002) (0.0002) (0.0016)

LLP BHC × Post Acquisition -0.0563** -0.0355 -0.0080 -0.0447**(0.0256) (0.0239) (0.0213) (0.0216)

LLP BHC × Post Acquisition × Treated 0.1964*** 0.1935*** 0.1921*** 0.1730***(0.0364) (0.0344) (0.0326) (0.0313)

Observations 54,075 54,075 54,075 54,075

R-Square 0.31 0.35 0.42 0.42

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year FE - - Yes -

Bank-Controls - - - Yes

State-Controls - - - Yes

48

Table 10: Robustness Test - LLP Residuals

This table compares loan loss provisions of target banks to those of their acquiring bank hold-ing companies over the 1976 - 2005 period. The dependent variable, LLP Target Residuals,corresponds to the residuals of a model adapted from Beatty and Liao (2014) that estimatethe level of loan loss provision of target banks. Post Acquisition is an indicator variable thatequals one after the acquisition of a target bank by another BHC, and zero otherwise. LLPBHC is equal to the average loan loss provision residuals of all banking subsidiaries composingthe acquiring BHC in the year before the acquisition, computed following the same model asthe dependent variable. All other variables are defined in Appendix A. Standard errors areclustered at the acquisition level. ***, **, and * indicate statistical significance at the 1%, 5%and 10% level, respectively.

Dependent Variable: LLP Target Residuals(1) (2) (3) (4)

LLP BHC Residual 0.1820*** 0.1806*** 0.0578*** 0.1727***(0.0136) (0.0139) (0.0139) (0.0139)

Post Acquisition -0.0005*** -0.0004*** -0.0003*** 0.0019(0.0001) (0.0001) (0.0001) (0.0015)

LLP BHC Residual × Post Acquisition 0.2170*** 0.2203*** 0.1882*** 0.2132***(0.0194) (0.0197) (0.0192) (0.0196)

Observations 56,046 56,046 56,046 56,046

R-Square 0.13 0.13 0.21 0.14

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year FE - - Yes -

State-Controls - - - Yes

49

Table 11: Robustness Test - LLP Weighted

This table compares loan loss provisions of target banks to those of their acquiring bank holdingcompanies over the 1976 - 2005 period. The dependent variable, LLP Target, corresponds tothe loan loss provision of target banks. Post Acquisition is an indicator variable that equals oneafter the acquisition of a target bank by another BHC, and zero otherwise. LLP BHC is equalto the weighted average loan loss provision of all banking subsidiaries composing the acquiringBHC in the year before the acquisition. We use subsidiary size as a weighting criterion. Allother variables are defined in Appendix A. Standard errors are clustered at the acquisitionlevel. ***, **, and * indicate statistical significance at the 1%, 5% and 10% level, respectively.

Dependent Variable: LLP Target(1) (2) (3) (4)

LLP BHC 0.3580*** 0.1933*** 0.0566*** 0.1716***(0.0127) (0.0129) (0.0132) (0.0134)

Post Acquisition -0.0018*** -0.0017*** -0.0015*** -0.0030*(0.0001) (0.0001) (0.0001) (0.0017)

LLP BHC × Post Acquisition 0.1449*** 0.1620*** 0.1622*** 0.1061***(0.0161) (0.0161) (0.0171) (0.0174)

Observations 56,046 56,046 56,046 56,046

R-Square 0.31 0.35 0.42 0.41

Acquisition FE Yes Yes Yes Yes

Year FE - Yes - Yes

State-Year FE - - Yes -

Bank-Controls - - - Yes

State-Controls - - - Yes

50

App

endix

A:

Vari

able

Definit

ion

and

Sourc

es

Var

iable

Nam

eV

aria

ble

Con

stru

ctio

nS

ourc

eL

LP

Loa

nlo

ssp

rovis

ion

(ite

mriad4230

)ov

erto

tal

loan

s(i

tem

rcfd2122

)F

ED

Cal

lR

epor

t

Pos

tA

cqu

isit

ion

Ind

icat

oreq

ual

toon

eaf

ter

the

acqu

isit

ion

dat

e,an

dze

root

her

wis

e.A

cqu

isit

ion

sar

eid

enti

fied

FE

DC

all

Rep

ort

usi

ng

chan

ges

inow

ner

ship

(ite

mrssd9348

)L

og(A

sset

s)L

ogar

ith

mof

ban

ks’

tota

las

sets

(ite

mrcfd2170

)F

ED

Cal

lR

epor

t

Lev

erag

eD

ebt

(ite

mrcfd3210

)ov

erto

tal

asse

ts(i

tem

rcfd2170

)F

ED

Cal

lR

epor

t

Loa

nG

row

thG

row

thra

teof

ban

ks’

tota

llo

ans

(ite

mrcfd2122

)F

ED

Cal

lR

epor

t

Loa

nC

once

ntr

atio

nH

HI

ofth

efo

llow

ing

loan

cate

gori

es:

real

esta

telo

ans

(ite

mrcfd1410

),ag

ricu

ltu

ral

loan

s(i

tem

rcfd1590

),F

ED

Cal

lR

epor

tC

&I

loan

s(i

tem

rcfd1600

)an

dlo

ans

toin

div

idu

als

(ite

mrcfd1975

)N

on-P

erfo

rmin

gL

oan

Gro

wth

Gro

wth

rate

ofb

anks’

non

-per

form

ing

loan

s,co

mp

ute

das

the

sum

ofit

emsrcfd1403

andrcfd1407

FE

DC

all

Rep

ort

Siz

eR

atio

Rat

ioof

acquir

ing

BH

C’s

tota

las

sets

tota

rget

BH

C’s

tota

las

sets

the

qu

arte

rp

rece

din

gth

eac

quis

itio

n.

FE

DC

all

Rep

ort

BH

C’s

tota

las

sets

are

the

sum

ofto

tal

asse

ts(i

tem

rcfd2170

)of

all

exis

tin

gsu

bsi

dia

ries

.

Pop

ula

tion

Log

ofto

tal

pop

ula

tion

BE

A

Per

son

alIn

com

eA

vera

gest

ate

per

son

alin

com

eB

EA

Per

son

alIn

com

eG

row

thC

han

gein

year

lyp

erso

nal

inco

me

BE

A

Sam

eM

SA

Ind

icat

oreq

ual

toon

eif

the

acqu

irin

gan

dta

rget

ban

ks

are

inth

esa

me

met

rop

olit

anst

atis

tica

lar

eaF

ED

Cal

lR

epor

t(i

tem

rssd9180

),an

dze

root

her

wis

e(S

ize

Acq

uir

ed)/

(Siz

eB

HC

)R

atio

ofta

rget

BH

C’s

tota

las

sets

over

acqu

irin

gB

HC

’sto

tal

asse

tson

the

qu

arte

rp

rece

din

gth

eac

qu

isit

ion

FE

DC

all

Rep

ort

BH

C’s

tota

las

sets

are

the

sum

ofto

tal

asse

ts(i

tem

rcfd2170

)of

all

exis

tin

gsu

bsi

dia

ries

.

Ind

epen

den

tT

oG

rou

pIn

dic

ator

vari

able

equ

alto

one

ifth

eta

rget

ban

kis

n’t

the

sub

sid

iary

ofa

ban

kin

ggr

oup

,e.

g.is

ind

epen

den

tF

ED

Cal

lR

epor

tan

dth

eac

qu

irin

gB

HC

own

sm

ore

than

one

sub

sid

iary

the

qu

arte

rb

efor

eth

eac

qu

isit

ion

.D

ista

nce

inL

LP

sD

iffer

ence

bet

wee

nth

eL

LP

sof

the

targ

etan

dth

eL

LP

sof

the

acqu

irin

gb

ank

(eve

nb

efor

eth

eB

HC

actu

ally

acquir

esth

eta

rget

)F

ED

Cal

lR

epor

t

51

App

endix

B:

Desc

ripti

ve

Sta

tist

ics

-M

atc

hin

g

This

table

pre

sents

the

des

crip

tive

stat

isti

csfo

rou

rsu

b-s

ample

sof

acquir

edban

ks

and

mat

ched

ban

ks.

We

const

ruct

our

sub-s

ample

ofm

atch

edban

ks

usi

ng

am

atch

ing

algo

rith

mth

atm

inim

izes

the

Mah

alan

obis

dis

tance

bet

wee

nob

serv

atio

ns

ofpre

-acq

uis

itio

nco

vari

ates

.B

ank

char

acte

rist

ics

are

mea

sure

din

the

end

ofth

eye

arb

efor

eth

eac

quis

itio

n.

We

imp

ose

the

requir

emen

tth

atta

rget

ban

ks

and

thei

rm

atch

edco

unte

rpar

tsb

elo

cate

din

the

sam

est

ate.

We

keep

3800

uniq

ue

acquis

itio

ns

inth

issa

mple

.T

he

num

ber

ofuniq

ue

acquis

itio

ns

inth

issa

mple

issm

alle

rth

anth

atof

our

mai

nan

alysi

sb

ecau

sew

ere

quir

eou

rm

atch

edsa

mple

ofban

ks

tob

est

atis

tica

lly

com

par

able

wit

hac

quir

edban

ks.

***,

**,

and

*in

dic

ate

stat

isti

cal

sign

ifica

nce

atth

e1%

,5%

and

10%

leve

l,re

spec

tive

ly.

Acq

uir

edB

anks

(N=

1900

)M

atch

edB

anks

(N=

1900

)V

aria

ble

Mea

nM

edia

nS.D

.M

ean

Med

ian

S.D

.D

iffer

ence

T-S

tati

stic

sL

LP

(%)

0.64

10.

408

0.62

50.

606

0.40

00.

588

-0.0

35(-

1.76

)

Lev

erag

e(%

)8.

437

8.05

92.

293

8.52

08.

191

2.06

60.

082

(1.1

7)

Log

Tot

alA

sset

s(M

illion

s)11

.133

11.0

091.

180

11.0

8710

.967

1.09

0-0

.045

(-1.

23)

Loa

nC

once

ntr

atio

n0.

422

0.40

10.

112

0.41

90.

398

0.10

6-0

.003

(-0.

94)

Loa

nG

row

th(%

)6.

197

6.09

113

.477

6.52

66.

358

11.7

080.

328

(0.8

0)

Non

-Per

form

ing

Loa

nG

row

th(%

)-1

0.73

3-9

.790

102.

413

-6.2

64-8

.556

105.

312

-4.4

69(-

1.31

)

Non

-Per

form

ing

Loa

nG

row

th(t

-1)

(%)

-15.

164

-14.

269

92.6

61-1

3.95

6-1

6.13

310

3.88

7-1

.208

(-0.

37)

52

Appendix C: Timing of Acquisition

This table shows the probability of the acquisition being realized depending on the distancebetween the distance in LLPs policies. Distance in LLP is the simple difference between theLLPs of the BHC that will acquire the target in the future and the LLPs of the target. Allother variables are defined in Appendix A. Standard errors are clustered at the acquisitionlevel. ***, **, and * indicate statistical significance at the 1%, 5% and 10% level, respectively.

Dependent Variable: Target Acquired(1) (2)

Distance in LLP 0.1115 0.0783(0.1790) (0.1792)

Leverage 0.5713*** 0.4258***(0.1399) (0.1473)

Loan Growth 0.1125*** 0.1274***(0.0148) (0.0147)

Loan Concentration 0.0024 0.0283(0.0207) (0.0307)

Real Estate Loan 0.0846** 0.0439(0.0355) (0.0392)

Non Performing Loans 0.0196 0.1238(0.1363) (0.1355)

Non Performing Loans (t-1) -0.4544*** -0.3613***(0.1256) (0.1261)

Non Performing Loans Growth -0.0041* -0.0051**(0.0022) (0.0022)

Observations 36,072 36,072

R-square 0.26 0.31

Bank FE Yes Yes

Year FE Yes Yes

State-Year FE - Yes

53